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href="/%E7%90%86%E8%A7%A3%E6%9C%80%E5%A4%A7%E4%BC%BC%E7%84%B6%E4%BC%B0%E8%AE%A1%E4%B8%8E%E6%9C%80%E5%A4%A7%E5%90%8E%E9%AA%8C%E6%A6%82%E7%8E%87/" title="理解最大似然估计与最大后验估计">     <img class="post_bg" src="https://cdn.jsdelivr.net/gh/SUNYunZeng/sources/img/mle_pic_1.png" onerror="this.onerror=null;this.src='/img/404.jpg'" alt="理解最大似然估计与最大后验估计"></a></div><div class="recent-post-info"><a class="article-title" href="/%E7%90%86%E8%A7%A3%E6%9C%80%E5%A4%A7%E4%BC%BC%E7%84%B6%E4%BC%B0%E8%AE%A1%E4%B8%8E%E6%9C%80%E5%A4%A7%E5%90%8E%E9%AA%8C%E6%A6%82%E7%8E%87/" title="理解最大似然估计与最大后验估计">理解最大似然估计与最大后验估计</a><div class="article-meta-wrap"><span class="article-meta"><i class="fas fa-thumbtack sticky"></i><span class="sticky">置顶</span><span class="article-meta__separator">|</span></span><span class="post-meta-date"><i class="far fa-calendar-alt"></i><span class="article-meta-label">发表于</span><time datetime="2021-04-01T03:25:32.000Z" title="发表于 2021-04-01 11:25:32">2021-04-01</time></span><span class="article-meta"><span class="article-meta__separator">|</span><i class="fas fa-inbox"></i><a class="article-meta__categories" href="/categories/%E7%AE%97%E6%B3%95/">算法</a></span></div><div class="content">最大似然估计最大似然估计（Maxiumum Likelihood Esimation, MLE）是频率学派对概率模型的一种参数估计方法。
它要解决的问题是，针对一定数量的独立重采样得到的样本，如何找到模型的参数 $\theta$，使得样本采样结果出现的概率最大。
具体来说，假设我们知道人身高的分布是正态分布，及概率密度函数的形式为：
f\left(x\right)=\frac1{\sqrt{2\pi}\sigma}exp\left(-\frac{\left(x-u\right)^2}{2\sigma^2}\right)其中，人身高的采样是独立同分布的，也就是你的身高不与别人的身高有关，且你们的身高服从同样的分布，即正态分布。
我们通过问卷调查，获得的一定数量人高的样本，然后估计身高分布模型的参数值方差　$\sigma$ 和均值　$\mu$。
似然函数数学上采用似然函数来解释样本已知，而模型参数未知的情况。
似然函数　$f\left(x\left|\theta\right.\right)$ 指样本　$x$　已知，而模型参数　$\theta$ 未知的函数。
假使模型的参数　$\theta$ ...</div></div></div><div class="recent-post-item"><div class="post_cover left_radius"><a href="/%E7%90%86%E8%A7%A3%E6%9C%80%E5%B0%8F%E4%BA%8C%E4%B9%98%E6%B3%95/" title="理解最小二乘法">     <img class="post_bg" src="https://pic2.zhimg.com/80/c93be818d85c341109372d4ce5367297_720w.jpg?source=1940ef5c" onerror="this.onerror=null;this.src='/img/404.jpg'" alt="理解最小二乘法"></a></div><div class="recent-post-info"><a class="article-title" href="/%E7%90%86%E8%A7%A3%E6%9C%80%E5%B0%8F%E4%BA%8C%E4%B9%98%E6%B3%95/" title="理解最小二乘法">理解最小二乘法</a><div class="article-meta-wrap"><span class="article-meta"><i class="fas fa-thumbtack sticky"></i><span class="sticky">置顶</span><span class="article-meta__separator">|</span></span><span class="post-meta-date"><i class="far fa-calendar-alt"></i><span class="article-meta-label">发表于</span><time datetime="2021-03-30T08:58:27.000Z" title="发表于 2021-03-30 16:58:27">2021-03-30</time></span><span class="article-meta"><span class="article-meta__separator">|</span><i class="fas fa-inbox"></i><a class="article-meta__categories" href="/categories/%E7%AE%97%E6%B3%95/">算法</a></span></div><div class="content">何为最小二乘
最小二乘法是十九世纪统计学的主题曲。从许多方面来看, 它之于统计学就相当于十八世纪的微积分之于数学。            ——史蒂芬·史蒂格勒的《The History of Statistics》

最小二乘是一种从一系列测量值中，计算最可能真值的数学方法。
小明真实的体重在探讨何为最小二乘时，先思考这个问题：
小明是一个健身爱好者，对自己的体重要求非常苛刻，因为这涉及到下一步的训练计划与饮食安排。为了能精确测量出自己的体重，他在市场上购买了9个都号称世界最准的体重秤（小明是个土豪）。
我们知道百分百准确的体重秤是不存在的，这种理想秤只存在于商家的口中。
那么，如何根据这9个比较准确的体重秤，测量出小明的真实体重呢？
可能的计算方案假设每个体重秤测出小明的体重值分别为 $y_1,y_2,y_3,…,y_9$，那么可能存在以下几个方案：

取平均值 $\frac{y_1+y_2+y_3+…+y_9}9$

取中位数 $medium({y_1,y_2,y_3,…,y_9})$

取极大值或极小值

取几何平均数

取调和平均数

掷色子

看心情


有很多种方案，到底哪 ...</div></div></div><div class="recent-post-item"><div class="post_cover right_radius"><a href="/%E5%86%8D%E8%A7%812021%EF%BC%8C%E4%BD%A0%E5%A5%BD2022%EF%BC%81/" title="再见2021，你好2022！">     <img class="post_bg" src="https://cdn.jsdelivr.net/gh/SUNYunZeng/sources/img/astronaut2.png" onerror="this.onerror=null;this.src='/img/404.jpg'" alt="再见2021，你好2022！"></a></div><div class="recent-post-info"><a class="article-title" href="/%E5%86%8D%E8%A7%812021%EF%BC%8C%E4%BD%A0%E5%A5%BD2022%EF%BC%81/" title="再见2021，你好2022！">再见2021，你好2022！</a><div class="article-meta-wrap"><span class="post-meta-date"><i class="far fa-calendar-alt"></i><span class="article-meta-label">发表于</span><time datetime="2022-01-01T04:18:24.000Z" title="发表于 2022-01-01 12:18:24">2022-01-01</time></span><span class="article-meta"><span class="article-meta__separator">|</span><i class="fas fa-inbox"></i><a class="article-meta__categories" href="/categories/%E6%9D%82%E8%AE%B0/">杂记</a></span></div><div class="content">这里有东西被加密了，需要输入密码查看哦</div></div></div><div class="recent-post-item"><div class="post_cover left_radius"><a href="/%E8%BF%88%E5%90%91%E6%96%B0%E9%98%B6%E6%AE%B5%EF%BC%9A%E5%AD%A6%E7%94%9F%E6%97%B6%E4%BB%A3%E7%9A%84%E8%90%BD%E5%B9%95/" title="迈向新阶段：学生时代的落幕">     <img class="post_bg" src="https://cdn.jsdelivr.net/gh/SUNYunZeng/sources/img/new_chapter.jpg" onerror="this.onerror=null;this.src='/img/404.jpg'" alt="迈向新阶段：学生时代的落幕"></a></div><div class="recent-post-info"><a class="article-title" href="/%E8%BF%88%E5%90%91%E6%96%B0%E9%98%B6%E6%AE%B5%EF%BC%9A%E5%AD%A6%E7%94%9F%E6%97%B6%E4%BB%A3%E7%9A%84%E8%90%BD%E5%B9%95/" title="迈向新阶段：学生时代的落幕">迈向新阶段：学生时代的落幕</a><div class="article-meta-wrap"><span class="post-meta-date"><i class="far fa-calendar-alt"></i><span class="article-meta-label">发表于</span><time datetime="2021-05-15T09:13:24.000Z" title="发表于 2021-05-15 17:13:24">2021-05-15</time></span><span class="article-meta"><span class="article-meta__separator">|</span><i class="fas fa-inbox"></i><a class="article-meta__categories" href="/categories/%E6%9D%82%E8%AE%B0/">杂记</a></span></div><div class="content">这里有东西被加密了，需要输入密码查看哦。</div></div></div><div class="recent-post-item"><div class="post_cover right_radius"><a href="/%E6%B5%85%E8%B0%88%E5%8C%BA%E5%9D%97%E9%93%BE%E4%B8%8E%E6%AF%94%E7%89%B9%E5%B8%81/" title="浅谈区块链与比特币">     <img class="post_bg" src="https://cdn.jsdelivr.net/gh/SUNYunZeng/sources/img/bitcoin.png" onerror="this.onerror=null;this.src='/img/404.jpg'" alt="浅谈区块链与比特币"></a></div><div class="recent-post-info"><a class="article-title" href="/%E6%B5%85%E8%B0%88%E5%8C%BA%E5%9D%97%E9%93%BE%E4%B8%8E%E6%AF%94%E7%89%B9%E5%B8%81/" title="浅谈区块链与比特币">浅谈区块链与比特币</a><div class="article-meta-wrap"><span class="post-meta-date"><i class="far fa-calendar-alt"></i><span class="article-meta-label">发表于</span><time datetime="2021-03-27T12:29:18.000Z" title="发表于 2021-03-27 20:29:18">2021-03-27</time></span><span class="article-meta"><span class="article-meta__separator">|</span><i class="fas fa-inbox"></i><a class="article-meta__categories" href="/categories/%E7%A7%91%E6%8A%80/">科技</a></span></div><div class="content">什么是区块链和比特币？有人说区块链是新一代价值互联网，有人说它是最慢的数据库，有人说区块链是一项类似于蒸汽机一样巨大的发明创新，也有人说区块链是一个巨大的庞氏骗局。到底什么是比特币，为什么它这么火？本篇文章将带你揭示比特币的前世今生。
区块链跟比特币的定义

区块链是一种去中心化的分布式记账本，而比特币是账本中所记录的数字货币的一种实现。

作为区块链的一种实际应用，比特币（Bitcoin）的原型是由中本聪（Satoshi Nakamoto）在2008年11月1日发表的一篇论文中提出的。比特币的真正诞生是在2009年1月3日。到今天为止，比特币的市值已经水涨船高，一枚比特币的价值高达55484.70美元，合362869.94元人民币！


比特币白皮书
比特币白皮书中文版

区块链要解决什么问题？既然区块链是一种去中心化的货币结算体系，那么原来的货币结算体系有什么问题呢？中本聪在论文的原文讲到：中心化的金融机构由于信任问题，不可避免在交易过程中存在仲裁与纠纷，就导致无法实现不可撤销的交易，在撤销过程中会导致额外的支付成本。当然，这只是现有中心化货币体系不可避免的问题，而区块链基于分布式 ...</div></div></div><div class="recent-post-item"><div class="post_cover left_radius"><a href="/npm-%E5%B8%B8%E7%94%A8%E5%91%BD%E4%BB%A4%E9%9B%86%E5%90%88/" title="npm 常用命令集合">     <img class="post_bg" src="https://w.wallhaven.cc/full/4y/wallhaven-4yqm8x.jpg" onerror="this.onerror=null;this.src='/img/404.jpg'" alt="npm 常用命令集合"></a></div><div class="recent-post-info"><a class="article-title" href="/npm-%E5%B8%B8%E7%94%A8%E5%91%BD%E4%BB%A4%E9%9B%86%E5%90%88/" title="npm 常用命令集合">npm 常用命令集合</a><div class="article-meta-wrap"><span class="post-meta-date"><i class="far fa-calendar-alt"></i><span class="article-meta-label">发表于</span><time datetime="2021-03-20T13:06:59.000Z" title="发表于 2021-03-20 21:06:59">2021-03-20</time></span><span class="article-meta"><span class="article-meta__separator">|</span><i class="fas fa-inbox"></i><a class="article-meta__categories" href="/categories/Web%E5%89%8D%E7%AB%AF/">Web前端</a></span></div><div class="content">对于 npm 的一些常用命令的汇总</div></div></div><div class="recent-post-item"><div class="post_cover right_radius"><a href="/%E5%8D%9A%E5%AE%A2%E4%B8%AD%E6%B7%BB%E5%8A%A0%E8%B6%B3%E8%BF%B9%E5%9C%B0%E5%9B%BE/" title="博客中添加足迹地图">     <img class="post_bg" src="https://sunyunzeng.com/%E5%8D%9A%E5%AE%A2%E4%B8%AD%E6%B7%BB%E5%8A%A0%E8%B6%B3%E8%BF%B9%E5%9C%B0%E5%9B%BE/%E5%9B%BE1.png" onerror="this.onerror=null;this.src='/img/404.jpg'" alt="博客中添加足迹地图"></a></div><div class="recent-post-info"><a class="article-title" href="/%E5%8D%9A%E5%AE%A2%E4%B8%AD%E6%B7%BB%E5%8A%A0%E8%B6%B3%E8%BF%B9%E5%9C%B0%E5%9B%BE/" title="博客中添加足迹地图">博客中添加足迹地图</a><div class="article-meta-wrap"><span class="post-meta-date"><i class="far fa-calendar-alt"></i><span class="article-meta-label">发表于</span><time datetime="2021-03-16T04:33:56.000Z" title="发表于 2021-03-16 12:33:56">2021-03-16</time></span><span class="article-meta"><span class="article-meta__separator">|</span><i class="fas fa-inbox"></i><a class="article-meta__categories" href="/categories/%E6%95%99%E7%A8%8B/">教程</a></span></div><div class="content">足迹地图效果展示足迹地图可以展示你过去到访过的地方，以及到访地点的介绍与图片记录，同时不同地标的半径大小表示了访问该地点的频率高低，如下图所示：


其中每个足迹图标都可以点击，点击会弹出左边的介绍栏及对应的介绍文字与图片。图片点击可以放大显示，如下图所示：


足迹地图使用方式克隆足迹地图项目首先，将足迹地图克隆到本地。
1git clone https:&#x2F;&#x2F;github.com&#x2F;SUNYunZeng&#x2F;FootprintMap.git
会得到如下所示的目录结构：
1234567891011121314FootprintMap├─ CNAME├─ css│  ├─ index.css│  └─ jquery-jvectormap-2.0.5.css├─ data│  └─ config.json├─ index.html├─ js│  ├─ index.js│  ├─ jquery-1.9.1.min.js│  ├─ jquery-jvectormap-2.0.5.min.js│  └─ jquery-jvectormap-cn-merc-en. ...</div></div></div><div class="recent-post-item"><div class="post_cover left_radius"><a href="/%E8%87%AA%E5%B7%B1%E7%AC%AC%E4%B8%80%E7%AF%87SCI%E6%96%87%E7%AB%A0-LSI-LSTM-%E4%B8%AA%E4%BD%93%E5%87%BA%E8%A1%8C%E7%9B%AE%E7%9A%84%E5%9C%B0%E9%A2%84%E6%B5%8B%E6%A8%A1%E5%9E%8B/" title="自己第一篇SCI文章： LSI-LSTM——个体出行目的地预测模型">     <img class="post_bg" src="https://sunyunzeng.com/%E8%87%AA%E5%B7%B1%E7%AC%AC%E4%B8%80%E7%AF%87SCI%E6%96%87%E7%AB%A0-LSI-LSTM-%E4%B8%AA%E4%BD%93%E5%87%BA%E8%A1%8C%E7%9B%AE%E7%9A%84%E5%9C%B0%E9%A2%84%E6%B5%8B%E6%A8%A1%E5%9E%8B/%E5%9B%BE3.png" onerror="this.onerror=null;this.src='/img/404.jpg'" alt="自己第一篇SCI文章： LSI-LSTM——个体出行目的地预测模型"></a></div><div class="recent-post-info"><a class="article-title" href="/%E8%87%AA%E5%B7%B1%E7%AC%AC%E4%B8%80%E7%AF%87SCI%E6%96%87%E7%AB%A0-LSI-LSTM-%E4%B8%AA%E4%BD%93%E5%87%BA%E8%A1%8C%E7%9B%AE%E7%9A%84%E5%9C%B0%E9%A2%84%E6%B5%8B%E6%A8%A1%E5%9E%8B/" title="自己第一篇SCI文章： LSI-LSTM——个体出行目的地预测模型">自己第一篇SCI文章： LSI-LSTM——个体出行目的地预测模型</a><div class="article-meta-wrap"><span class="post-meta-date"><i class="far fa-calendar-alt"></i><span class="article-meta-label">发表于</span><time datetime="2021-03-15T04:51:50.000Z" title="发表于 2021-03-15 12:51:50">2021-03-15</time></span><span class="article-meta"><span class="article-meta__separator">|</span><i class="fas fa-inbox"></i><a class="article-meta__categories" href="/categories/%E7%AE%97%E6%B3%95/">算法</a></span></div><div class="content">内容导读个体驾驶目的地预测对于个性化服务推荐、出行导航、交通调度等基于位置的服务具有重要的参考意义。然而，个体出行不仅具有长时间依赖性，而且受到出行空间上下文的影响，使得精准实时的驾驶目的地预测面临较大挑战。目前，长时间依赖关系建模已存在一定的技术手段，但空间因素对出行过程的影响仍然较少被考虑到。事实上，出发地与沿途的城市功能区、路网关键节点均一定程度反映并影响着司机的出行意图。为此，本文提出一种基于注意力机制和长短期记忆（LSTM）的个体驾驶目的地实时预测模型LSI-LSTM。该模型在兼顾个体出行长时间依赖性学习的同时，从轨迹点的位置语义、轨迹点与目的地间的空间关联关系两个维度建模空间上下文。具体来说，使用高德POI和TF-IDF算法建立轨迹语义提取方法t-LSE，以获得轨迹点所在区域的城市功能区类型；基于转向角、速度、已行驶距离三类驾驶状态评价轨迹点的位置重要性，并利用轨迹空间注意力机制t-SAM进一步捕获与潜在目的地有强空间关联的轨迹点。基于上述城市功能区类型和位置重要性，LSI-LSTM对轨迹点的空间上下文进行建模，在不需要外部路网数据的情况下实现细粒度空间出行特征的学习。基于 ...</div></div></div><div class="recent-post-item"><div class="post_cover right_radius"><a href="/%E5%86%8D%E8%A7%812020%EF%BC%8C%E4%BD%A0%E5%A5%BD2021%EF%BC%81/" title="再见2020，你好2021！">     <img class="post_bg" src="https://cdn.jsdelivr.net/gh/SUNYunZeng/sources/img/astronaut1.png" onerror="this.onerror=null;this.src='/img/404.jpg'" alt="再见2020，你好2021！"></a></div><div class="recent-post-info"><a class="article-title" href="/%E5%86%8D%E8%A7%812020%EF%BC%8C%E4%BD%A0%E5%A5%BD2021%EF%BC%81/" title="再见2020，你好2021！">再见2020，你好2021！</a><div class="article-meta-wrap"><span class="post-meta-date"><i class="far fa-calendar-alt"></i><span class="article-meta-label">发表于</span><time datetime="2021-01-01T10:18:31.000Z" title="发表于 2021-01-01 18:18:31">2021-01-01</time></span><span class="article-meta"><span class="article-meta__separator">|</span><i class="fas fa-inbox"></i><a class="article-meta__categories" href="/categories/%E6%9D%82%E8%AE%B0/">杂记</a></span></div><div class="content">这里有东西被加密了，需要输入密码查看哦。</div></div></div><nav id="pagination"><div class="pagination"><span class="page-number current">1</span><a class="page-number" href="/page/2/#content-inner">2</a><span class="space">&hellip;</span><a class="page-number" href="/page/18/#content-inner">18</a><a class="extend next" rel="next" href="/page/2/#content-inner"><i class="fas fa-chevron-right fa-fw"></i></a></div></nav></div><div class="aside-content" 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